mirror of
https://github.com/prometheus/prometheus.git
synced 2024-11-18 11:34:05 -08:00
189 lines
7.6 KiB
Plaintext
189 lines
7.6 KiB
Plaintext
# Two histograms with 4 buckets each (x_sum and x_count not included,
|
|
# only buckets). Lowest bucket for one histogram < 0, for the other >
|
|
# 0. They have the same name, just separated by label. Not useful in
|
|
# practice, but can happen (if clients change bucketing), and the
|
|
# server has to cope with it.
|
|
|
|
# Test histogram.
|
|
load 5m
|
|
testhistogram_bucket{le="0.1", start="positive"} 0+5x10
|
|
testhistogram_bucket{le=".2", start="positive"} 0+7x10
|
|
testhistogram_bucket{le="1e0", start="positive"} 0+11x10
|
|
testhistogram_bucket{le="+Inf", start="positive"} 0+12x10
|
|
testhistogram_bucket{le="-.2", start="negative"} 0+1x10
|
|
testhistogram_bucket{le="-0.1", start="negative"} 0+2x10
|
|
testhistogram_bucket{le="0.3", start="negative"} 0+2x10
|
|
testhistogram_bucket{le="+Inf", start="negative"} 0+3x10
|
|
|
|
|
|
# Now a more realistic histogram per job and instance to test aggregation.
|
|
load 5m
|
|
request_duration_seconds_bucket{job="job1", instance="ins1", le="0.1"} 0+1x10
|
|
request_duration_seconds_bucket{job="job1", instance="ins1", le="0.2"} 0+3x10
|
|
request_duration_seconds_bucket{job="job1", instance="ins1", le="+Inf"} 0+4x10
|
|
request_duration_seconds_bucket{job="job1", instance="ins2", le="0.1"} 0+2x10
|
|
request_duration_seconds_bucket{job="job1", instance="ins2", le="0.2"} 0+5x10
|
|
request_duration_seconds_bucket{job="job1", instance="ins2", le="+Inf"} 0+6x10
|
|
request_duration_seconds_bucket{job="job2", instance="ins1", le="0.1"} 0+3x10
|
|
request_duration_seconds_bucket{job="job2", instance="ins1", le="0.2"} 0+4x10
|
|
request_duration_seconds_bucket{job="job2", instance="ins1", le="+Inf"} 0+6x10
|
|
request_duration_seconds_bucket{job="job2", instance="ins2", le="0.1"} 0+4x10
|
|
request_duration_seconds_bucket{job="job2", instance="ins2", le="0.2"} 0+7x10
|
|
request_duration_seconds_bucket{job="job2", instance="ins2", le="+Inf"} 0+9x10
|
|
|
|
# Different le representations in one histogram.
|
|
load 5m
|
|
mixed_bucket{job="job1", instance="ins1", le="0.1"} 0+1x10
|
|
mixed_bucket{job="job1", instance="ins1", le="0.2"} 0+1x10
|
|
mixed_bucket{job="job1", instance="ins1", le="2e-1"} 0+1x10
|
|
mixed_bucket{job="job1", instance="ins1", le="2.0e-1"} 0+1x10
|
|
mixed_bucket{job="job1", instance="ins1", le="+Inf"} 0+4x10
|
|
mixed_bucket{job="job1", instance="ins2", le="+inf"} 0+0x10
|
|
mixed_bucket{job="job1", instance="ins2", le="+Inf"} 0+0x10
|
|
|
|
# Quantile too low.
|
|
eval instant at 50m histogram_quantile(-0.1, testhistogram_bucket)
|
|
{start="positive"} -Inf
|
|
{start="negative"} -Inf
|
|
|
|
# Quantile too high.
|
|
eval instant at 50m histogram_quantile(1.01, testhistogram_bucket)
|
|
{start="positive"} +Inf
|
|
{start="negative"} +Inf
|
|
|
|
# Quantile value in lowest bucket, which is positive.
|
|
eval instant at 50m histogram_quantile(0, testhistogram_bucket{start="positive"})
|
|
{start="positive"} 0
|
|
|
|
# Quantile value in lowest bucket, which is negative.
|
|
eval instant at 50m histogram_quantile(0, testhistogram_bucket{start="negative"})
|
|
{start="negative"} -0.2
|
|
|
|
# Quantile value in highest bucket.
|
|
eval instant at 50m histogram_quantile(1, testhistogram_bucket)
|
|
{start="positive"} 1
|
|
{start="negative"} 0.3
|
|
|
|
# Finally some useful quantiles.
|
|
eval instant at 50m histogram_quantile(0.2, testhistogram_bucket)
|
|
{start="positive"} 0.048
|
|
{start="negative"} -0.2
|
|
|
|
|
|
eval instant at 50m histogram_quantile(0.5, testhistogram_bucket)
|
|
{start="positive"} 0.15
|
|
{start="negative"} -0.15
|
|
|
|
eval instant at 50m histogram_quantile(0.8, testhistogram_bucket)
|
|
{start="positive"} 0.72
|
|
{start="negative"} 0.3
|
|
|
|
# More realistic with rates.
|
|
eval instant at 50m histogram_quantile(0.2, rate(testhistogram_bucket[5m]))
|
|
{start="positive"} 0.048
|
|
{start="negative"} -0.2
|
|
|
|
eval instant at 50m histogram_quantile(0.5, rate(testhistogram_bucket[5m]))
|
|
{start="positive"} 0.15
|
|
{start="negative"} -0.15
|
|
|
|
eval instant at 50m histogram_quantile(0.8, rate(testhistogram_bucket[5m]))
|
|
{start="positive"} 0.72
|
|
{start="negative"} 0.3
|
|
|
|
# Aggregated histogram: Everything in one.
|
|
eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) by (le))
|
|
{} 0.075
|
|
|
|
eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[5m])) by (le))
|
|
{} 0.1277777777777778
|
|
|
|
# Aggregated histogram: Everything in one. Now with avg, which does not change anything.
|
|
eval instant at 50m histogram_quantile(0.3, avg(rate(request_duration_seconds_bucket[5m])) by (le))
|
|
{} 0.075
|
|
|
|
eval instant at 50m histogram_quantile(0.5, avg(rate(request_duration_seconds_bucket[5m])) by (le))
|
|
{} 0.12777777777777778
|
|
|
|
# Aggregated histogram: By job.
|
|
eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) by (le, instance))
|
|
{instance="ins1"} 0.075
|
|
{instance="ins2"} 0.075
|
|
|
|
eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[5m])) by (le, instance))
|
|
{instance="ins1"} 0.1333333333
|
|
{instance="ins2"} 0.125
|
|
|
|
# Aggregated histogram: By instance.
|
|
eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) by (le, job))
|
|
{job="job1"} 0.1
|
|
{job="job2"} 0.0642857142857143
|
|
|
|
eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[5m])) by (le, job))
|
|
{job="job1"} 0.14
|
|
{job="job2"} 0.1125
|
|
|
|
# Aggregated histogram: By job and instance.
|
|
eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) by (le, job, instance))
|
|
{instance="ins1", job="job1"} 0.11
|
|
{instance="ins2", job="job1"} 0.09
|
|
{instance="ins1", job="job2"} 0.06
|
|
{instance="ins2", job="job2"} 0.0675
|
|
|
|
eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[5m])) by (le, job, instance))
|
|
{instance="ins1", job="job1"} 0.15
|
|
{instance="ins2", job="job1"} 0.1333333333333333
|
|
{instance="ins1", job="job2"} 0.1
|
|
{instance="ins2", job="job2"} 0.1166666666666667
|
|
|
|
# The unaggregated histogram for comparison. Same result as the previous one.
|
|
eval instant at 50m histogram_quantile(0.3, rate(request_duration_seconds_bucket[5m]))
|
|
{instance="ins1", job="job1"} 0.11
|
|
{instance="ins2", job="job1"} 0.09
|
|
{instance="ins1", job="job2"} 0.06
|
|
{instance="ins2", job="job2"} 0.0675
|
|
|
|
eval instant at 50m histogram_quantile(0.5, rate(request_duration_seconds_bucket[5m]))
|
|
{instance="ins1", job="job1"} 0.15
|
|
{instance="ins2", job="job1"} 0.13333333333333333
|
|
{instance="ins1", job="job2"} 0.1
|
|
{instance="ins2", job="job2"} 0.11666666666666667
|
|
|
|
# A histogram with nonmonotonic bucket counts. This may happen when recording
|
|
# rule evaluation or federation races scrape ingestion, causing some buckets
|
|
# counts to be derived from fewer samples. The wrong answer we want to avoid
|
|
# is for histogram_quantile(0.99, nonmonotonic_bucket) to return ~1000 instead
|
|
# of 1.
|
|
|
|
load 5m
|
|
nonmonotonic_bucket{le="0.1"} 0+1x10
|
|
nonmonotonic_bucket{le="1"} 0+9x10
|
|
nonmonotonic_bucket{le="10"} 0+8x10
|
|
nonmonotonic_bucket{le="100"} 0+8x10
|
|
nonmonotonic_bucket{le="1000"} 0+9x10
|
|
nonmonotonic_bucket{le="+Inf"} 0+9x10
|
|
|
|
# Nonmonotonic buckets
|
|
eval instant at 50m histogram_quantile(0.99, nonmonotonic_bucket)
|
|
{} 0.989875
|
|
|
|
# Buckets with different representations of the same upper bound.
|
|
eval instant at 50m histogram_quantile(0.5, rate(mixed_bucket[5m]))
|
|
{instance="ins1", job="job1"} 0.15
|
|
{instance="ins2", job="job1"} NaN
|
|
|
|
eval instant at 50m histogram_quantile(0.75, rate(mixed_bucket[5m]))
|
|
{instance="ins1", job="job1"} 0.2
|
|
{instance="ins2", job="job1"} NaN
|
|
|
|
eval instant at 50m histogram_quantile(1, rate(mixed_bucket[5m]))
|
|
{instance="ins1", job="job1"} 0.2
|
|
{instance="ins2", job="job1"} NaN
|
|
|
|
load 5m
|
|
empty_bucket{le="0.1", job="job1", instance="ins1"} 0x10
|
|
empty_bucket{le="0.2", job="job1", instance="ins1"} 0x10
|
|
empty_bucket{le="+Inf", job="job1", instance="ins1"} 0x10
|
|
|
|
eval instant at 50m histogram_quantile(0.2, rate(empty_bucket[5m]))
|
|
{instance="ins1", job="job1"} NaN |